Freespace detection in a driver assistance system of a motor vehicle with a neural network

ABSTRACT

The invention relates to a method for operating a driver assistance system ( 2 ) of a motor vehicle ( 1 ), including a) Capturing an environment ( 4 ) of the motor vehicle ( 1 ) by a capturing device ( 3 ) of the driver assistance system ( 2 ); b) Detecting an accessible freespace ( 6 ) in the captured environment ( 4 ) by a computing device ( 5 ) of the driver assistance system ( 2 ); c) Detecting and Classifying at least one object ( 7   a - 7   e ) in the captured environment ( 4 ) that is located at a border ( 8 ) of the freespace ( 6 ) by a neural network ( 9 ) of the driver assistance system ( 2 ); d) Assigning a part ( 10   a - 10   e ) of the border ( 8 ) of the freespace ( 6 ) to the detected and classified object ( 7   a - 7   e ); and e) Categorizing a part ( 11   a - 11   e ) of the freespace ( 6 ) adjacent to the part ( 10   a - 10   e ) of the border ( 8 ) that is assigned to the detected and classified object ( 7   a - 7   e ) in dependence upon the class of that classified object ( 7   a - 7   e ), so as to enable improved safety in driving.

FIELD OF THE INVENTION

The invention relates to a method for operation a driver assistancesystem of a motor vehicle, including the method steps of capturing anenvironment of the motor vehicle by a capturing device of the driverassistance system and of detecting a freespace accessible to the vehiclein the captured environment by a computing device of the driverassistance system, where the freespace is preferably adjacent to themotor vehicle. The invention also relates to a driver assistance systemfor a motor vehicle, with a capturing device for capturing anenvironment of the motor vehicle and with a computing device fordetecting a freespace accessible to the motor vehicle in the capturedenvironment.

BACKGROUND

The detection of accessible freespace, that is, the detection of an areain the environment of the motor vehicle that is free of obstacles andhence can be driven on or accessed by the motor vehicle is important fordriver assistance systems. This is particularly true when it comes topartially or fully automated driving, but may also be valuable forproviding for instance a warning to a driver in manual driving. Inparticular in parking scenarios, this is very important, as many staticand dynamic obstacles are present in such a scenario. There, thedetection of freespace is not just the logical reverse of detecting anobstacle. Basically, any type of object detector detects objects withina specific region of interest (ROI) of processing. However, any objectdetector has a limited performance, that means, there are always missedobjects. Therefore, the object detector alone cannot be used to definethe freespace as a missed object could be within the presumed freespace.

Consequently, on one hand, freespace detection is used to erase dynamicobstacles in a map of the environment stored in the driver assistancesystem which cannot be tracked in the map of the environment. This meansthat a good freespace detection will erase dynamic obstacles in theirprevious position very quickly without erasing static obstacleinformation. On the other hand, a freespace detection shall erasefalsely detected obstacles. A driver assistance system needs to ensurekeeping static obstacles in the environmental map, quickly erasing theprevious position of a dynamic obstacle and reducing false positives byquickly erasing falsely detected obstacles. Furthermore, freespacedetection can be used to partially correct the error of the ego vehicleodometry calculation, when a re-detection of a static obstacle does notgive the same position. In this case, in the freespace detection, theobstacle from previous detections must correctly be erased. Usually sucha freespace detection is based on depth or distance information fordifferent sensor channels and/or different sectors that can extendfan-like from the position of the capturing device, for instance acamera, into the environment. Such a freespace detection is, forinstance, described in the DE 10 2015 115 012 A1.

SUMMARY OF THE INVENTION

It is an object of the present invention to enable improved safety indriving, in particular semi-automated or fully automated driving with amotor vehicle.

This object is solved by the subject matters of the independent claims.Advantageous embodiments are apparent from the dependent claims, thedescription and the FIGURE.

One aspect of the invention relates to a method for operating a driverassistance system, in particular a parking assistance system, of a motorvehicle, including several method steps. One method step is capturing anenvironment of a capturing device of the driver assistance system. Inparticular, the capturing device may include a camera device with one orseveral, for instance four, cameras. Here, it is advantageous if thecapturing device includes one or more hardware accelerators enabling forinstance a processing of 16 mega pixels per second or more. To this end,the capturing device may comprise for instance four cameras with atleast 1 mega pixel each, which have a refresh rate of at least 15 framesper second, as described below.

Another method step is detecting an accessible freespace in the capturedenvironment by a computing device of the driver assistance system. Inparticular, the freespace is adjacent to the motor vehicle. Thedetecting can be done by determining the distance from the car forseveral sectors that extend fan-like from the capturing device into theenvironment of the motor vehicle as in the state of the art cited above,for example. Such a set of distance can also be called a depth map asthe distances describe the depth of the accessible freespace from thevehicle's point of view. Optionally, a segmentation algorithm using theknown perceptual grouping can be used to enhance such a depth map.Preferably, a so-called dense depth map based on millions instead of theconventional thousands of flow vectors in a sparse depth map is used inthe freespace detection. So, such a dense depth map is based on aso-called dense optical flow with preferably one megapixel or more. Bycontrast, the traditional sparse optical flow features only a kilopixel. Because of the computational complexity of processing millions offlow vectors, it is efficient to tile them and distribute themadaptively across different tiles of the respective images according tothe scene complexity. For instance, regions on the sky are very smoothand fewer feature or flow vectors can be allocated here based on theconfidence level of each, where the confidence level is in particularprovided as a posterior estimate.

Furthermore, prior knowledge based learning may be acquired as well,that is, regions of sky, road and other regions may be learned via thespatial layout of typical distributions in the optical flow. Inparticular, these flow vectors may be separated into static and dynamicvectors. This can be preferably be effected after the tiling of the flowvectors. The static and dynamic vectors may be calculated usingdifferent constraints, for instance an epi-polar geometric constraintand/or a spatial consistency constraint and/or a temporal propagationconstraint. The moving points may be separated out and passed through alattice-based clustering algorithm. In contrast to a regular clusteringmethod, here the points may be distributed on a regular lattice andexploit the partial ordering of the points in the lattice to produce abetter clustering algorithm. The static points can be processed by astructure from motion pipeline (SFM-pipeline). Therein, the relativepose of the camera may be calculated by a combination of planarhomography based on point to the ground plane and an essential matrixcomputation for non-ground points.

This complimentary combination provides robustness to the estimate whichis advantageous for the accuracy of the next steps in thestructure-from-motion-pipeline. A 3D-reconstruction may be computedusing a re-projection error metric and iterative least squares.

Typically, such a 3D-reconstruction is very noisy. Due to the ambiguityof low textures regions, parallel moving objects, or noisy triangulationestimates, there are also a lot of holes or uncovered areas in thereconstruction. The holes can even dominate in terms of number of flowvectors relative to the correct estimates in a difficult scenario. It istherefore advantageous, to use a total variation based regularizationalgorithm which can fill in the holes and form a smoother depth map.This is very helpful for getting a precise freespace estimate. Forfreespace detection, it is important to reconstruct the ground plane asprecise as possible. In particular, using a dense optical flow and whatcan be called a dense depth map (in particular in the above specifiedmeaning) can, when, for instance, averaging over time, lead to a smoothand precise ground plane reconstruction which may not be possible with asparse representation of the environment.

Another method step is detecting and/or classifying at least one objectin the captured environment that is located at a border of thefreespace, in particular the detected freespace, by a neural network ofor in the driver assistance system. So, at least one object, that is,one object or several objects, located adjacent to the detectedfreespace are detected and classified. Here, the neural network may beused for detecting and classifying an object with the known “slidingwindow object detection” for detecting pedestrians, cyclist andvehicles. This sliding window object detection is computationalintensive and one of the challenges is to design an efficient networkthat implements the method with reasonable computational cost. As onlythe objects close to the border of the detected freespace are relevantfor the proposed method, for example the sliding window object detectioncan be applied while being restricted to an area with a pre-set maximaldistance from the border of the detected freespace. This saves a lot ofcomputational cost.

As the usual approaches using region convolution neural networks (R-CNN)and cascade convolution neural networks (C-CNN) leave holes in featuremaps, they are not suitable for the proposed method. Instead, inparticular, a generic function learning approach is proposed where theneural network is trained for recognizing abstract features instead of afull object classification. In contrast to a full object classification,where, for example the contour of pedestrians, cyclists and vehiclesneeds to be learned, in the present approach the neural network onlyneeds to learn to recognize the parts of the object that are located atthe border of the freespace, hence, for example, it is sufficient torecognize the feet of a pedestrian or wheels of a cyclist or vehicle.Therefore, the abstract features the neural network needs to learn arenot necessarily linked to images or ideas as they are perceived orunderstood by humans, hence the name “abstract feature”.

We call this generic function learning approach, since this reduces theamount and/or quality of necessary learning. This can help (an engineof) the driver assistance system to better handle noise and resolveconfusion between two objects by synthesising the available informationholistically. So the neural network can produce a feature map, wherefeatures, in particular the above mentioned abstract features, areassigned to different subsections of the captured environment, forexample, different subsections of a camera image. Based on thesefeatures, the objects may be detected and/or classified in thatrespective subsection with a respective confidence value. So, if manyfeatures of one class of objects are activated or detected in thefeature map for a specific area or subsection, this subsection may beclassified as belonging to the respective class of objects. Note that aspecific subsection can be classified as belonging to different classesaccording to the activated or detected features in that subsection withdifferent probabilities represented by the confidence value. So, theclassification does not need to be limited to one class per objectand/or subsection. In contrast, the feature map may be used to assign arespective probability for more than one object class for the respectivesubsection or area. So, based on the feature map, a probability for apedestrian in a specific subsection may be determined as 55% and aprobability for a cyclist in that very same subsection may be determinedas 35%, for instance.

Another method step is assigning a part of the border that is adjacentto the detected and/or classified object to the detected and classifiedobject by the computing device. This assignment can be part of theclassifying. When detecting and/or classifying several objects,different parts of the border may hence be assigned to differentobjects. In particular, one specific part of the border may only beassigned or assignable to one single object.

Another method step is categorizing a part of the freespace adjacent tothe part of the border that is assigned to the detected and/orclassified object in dependence of the class of that (detected and/orclassified) object which is adjacent to the part of the border (andhence the part of the freespace to be categorized) by the computingdevice. So, different parts of the freespace are categorized asbelonging to different categories, leading to what can be called a“semantic freespace” or “semantic freespace detection”, where freespaceis not considered to be uniform freespace that is accessible ordriveable by the motor vehicle, but rather to be a freespace withdifferent parts or sub areas having different adjectives describingcharacteristics of the freespace and that are summarized in the term“semantic” that indicate to the driver assistance system how the motorvehicle should behave when entering and/or approaching the differentparts of freespace. Here, noise can be smoothed out in the estimation,that is, detection and/or classification of objects and measurement ofdepth, that is, of respective distances assigned to different sectionsof the freespace, in spatial and temporal dimensions via spatio-temporalfusion of the categorized freespace. In the spatio-temporal fusion datafrom different cameras as capturing units may be fused. The assumptionthat the data of different time steps are not independent results inadvantages of the spatio-temporal fusion. Here, for instance aconditional random field (CRF) may be used for capturing transitions andthe combination of the so-called depth map with the feature map of theneural network. Conditional random fields also lend elegantly toefficient recursive estimation using loop belief propagation and can befused with other probabilistic graphical models for subsequent sensorsor other sensors and inference engines. This leads to a stable androbust semantic freespace detection and/or robust semantic freespacemap.

The proposed method provides what is called here a semantic freespacedetection, that is, a freespace detection where regions or parts of thefreespace are categorized individually according to the object at theedge or border of the respective region of the freespace instead ofdetecting the freespace as one single region with homogeneous propertiesor characteristics. The output of the freespace detection may be adistance from the motor vehicle for each of a number of sectors thefreespace is divided into. Such a distance may be referred to as depth,hence the detected freespace may be referred to as or including a depthmap. The sectors may be organized fan-like starting from the position ofthe capturing device, for instance the camera and extend towards theedge or border of the freespace determined by the respective objects ora range of the capturing device.

As for the so-called semantic freespace detection, the freespacecomprises semantics, that is, further information concerning respectiveregions or parts or subsections of the freespace as well, meaning therewill be an object detection associated to the freespace detection.Knowing the regions or parts of the freespace helps to adapt to movingobjects and estimate the risk of collision which enables bettermanoeuvring algorithms for automated driving, for instance. It alsohelps to predict or anticipate changes in freespace for temporalprocessing. For instance, fast moving objects or vehicles may changefreespace drastically.

In the proposed method, neural learning recognition, for instance deeplearning recognition, is combined with classical algorithms likestructure-from-motion segmentation and/or color segmentation forobtaining a freespace detection where different parts or regions of thefreespace are associated with different properties and hence enable adifferentiated driving behaviour adapted to the respective environmentand the properties of the objects detected therein. So, for instance,different speed limits may be set for the motor vehicle moving in or toparts or regions of the freespace that are adjacent to differentobjects. For example in parts of the freespace that are adjacent to apedestrian, the motor vehicle may not be allowed to drive faster thanwalking speed. In contrast, in parts of the freespace that are adjacentto cars, this speed limit may be not applied for instance.

Altogether, the novel approach of using a neural network for classifyingobjects and, in consequence, categorizing different parts or regions ofthe freespace in combination with the conventional (depth) informationfor computing the freespace provides an accurate and reliable freespacedetection in form of detecting the semantic freespace. It is evenpossible to double-check the conventional freespace detection with theresults of the neural network. A dense feature detection can help toprovide full information about smooth textures on surfaces, which is notpossible in the conventional sparse feature detection due to lack ofpoints on the smooth surface. Hence, the proposed method is inparticular effective when using dense feature detection.

In a preferred embodiment, it is provided that a part of the freespaceadjacent to another part of the border, that is, a part of the borderthat is not assigned to the object or the objects, in particular a partof the freespace adjacent to the rest of the border, is categorized assafely accessible freespace by the computing device. This gives theadvantage of a reasonable standard classification for freespace which islimited by the range of the capturing device.

Herein, the detecting of the accessible freespace may in particular beeffected before or after detecting and/or classifying the object(s).Detecting the accessible freespace before give the advantage that onlyobjects at the border or edge of the detected freespace need to bedetected and/or classified.

In another advantageous embodiment, it is provided that a respectiverisk and/or danger is assigned to the categories of freespace that thepart of the freespace can be categorized into, in particular a risk fora collision with the object adjacent to the respective part of thefreespace. This gives the advantage that the categories are particularlyhelpful for driving, in particular the semi-automated or fully automateddriving.

Herein, it may be provided that the categories for the freespacecomprise a category for safely accessible freespace and/or a categoryfor potentially dangerous freespace, where for instance a first speedlimit is set, and/or a category for dangerous freespace, where forinstance a second, as compared to the first speed limit preferablylower, speed limit is set or driving into is prohibited. So, forinstance by means of the categories, the motor vehicle can be hinderedfrom driving into a freespace that is adjacent to a pedestrian as thepedestrian is particularly vulnerable. To this end, the part of thefreespace adjacent to an object classified as pedestrian could becategorized as dangerous freespace. The part of the freespace adjacentto an object classified as car could for example be categorized aspotentially dangerous freespace, where a speed limit applies as cars maymove fast but are less vulnerable than a pedestrian. A part of thefreespace adjacent to an object classified as stationary object, forinstance a tree, may be categorized as safely accessible freespace, as astationary object will not move. The categories may also be linked to adepth or distance information. So, the freespace may be categorized alsobased on the respective distance of the detected and/or classifiedobject from the motor vehicle.

This gives the advantage, that different parts of the freespace areassociated with different characteristics or properties. This allows toadapt the driving behaviour, in particular the driving behaviour in thesemi-automated or fully automated driving, to the situation at hand in avery specific, adaptive way leading to improved safety and comfort.

In another advantageous embodiment, it is provided that the classes forthe object comprise a (at least one) class for static obstacles, inparticular a class for obstacles that can be driven over, in particularcurbs, and/or a (at least one) class for obstacles that cannot be drivenover, and/or a (at least one) class for dynamic obstacles, in particulara class for pedestrians and/or a class for cyclists and/or a class formotor vehicles such as motorbikes, cars or lorries. These classes forthe object have been proven specifically useful for a meaningful andhence helpful semantic freespace detection improving driving safety.

In another advantageous embodiment it is provided that in theclassifying of the at least one object, more than one class is assignedto the classified object and a confidence value (that represents theprobability that the respective class assigned to the classified objectis true) is assigned to each class assigned to the object, and thecategorizing of the part of the freespace is effected in dependence uponthe classes of that classified object and the respective confidencevalues. This gives the advantage that the categorizing is specificallyflexible and reliable.

Herein, it may be provided that in the categorizing of the part of thefreespace the confidence values for the classes of the classified objectare compared, and the freespace is categorized into the category for thefreespace that corresponds to the class with the highest confidencevalue if this (highest) confidence value differs from the second highestconfidence value of the classes assigned to the classified object morethan a pre-set threshold, and, in particular, the freespace iscategorized into the category that the highest respective risk isassigned to if the highest confidence value differs from the secondhighest confidence value is less than that threshold. So, for example,if the pre-set threshold is 10%, if an object is classified as belongingto the object class of motor vehicles with the probability of 60% andbelonging to the object class of pedestrian with a probability of 40%,the freespace will be categorized into the category that corresponds tothe motor vehicle class in this example. For instance, the freespace maybe categorized as potentially dangerous freespace. If, in this example,on the other hand the confidence value for the classified objectindicates a probability of 51% for the object being a motor vehicle and49% for the object being a pedestrian, the difference of the confidencevalues is smaller than the pre-set threshold here. Hence, the freespaceis categorized into the category with the highest respective riskassigned to in the present example. That is, in the present example thefreespace adjacent to said object will be categorized as dangerousfreespace (in spite of the object being classified as a motor vehicleand the category of potentially dangerous freespace would be moreappropriate according to the confidence values).

This gives the advantage that the categorizing can be more flexible andin ambiguous situations a potential risk for collision can be minimized.

In another embodiment, it is provided that the capturing devicecomprises several cameras and the detecting of the accessible freespaceas well as the detecting and/or classifying of the at least one objectas well as the assigning of the part of the border and the categorizingof the part of the freespace is effected for pictures of the capturedenvironment that are taken by different cameras, and results of thecategorization that are based on different pictures of the same and/ordifferent cameras are fused into an overall result of categorisationwith the assumption that the results are dependent on each other asdescribed by a pre-set rule. Here, in particular, the fusing is effectedusing a conditional random field. This can be called a spatio-temporalfusion of the pictures.

This gives the advantage that the data, that is, the results that arebased on the individual single pictures can be used to enhance theresults when processing subsequent pictures of the same camera orpictures of the other cameras. Hence, the effectiveness of the method isimproved.

In yet another embodiment, the neural network comprises a convolutionalneural network, in particular a deep neural network with multiple hiddenlayers. This gives the advantage that the neural network is specificallyfit for detecting and/or classifying objects in for instance pictures.Hence, the method is particularly effective.

In another advantageous embodiment it is provided that the neuralnetwork is trained to classify the object, in particular to detect andto classify the object, (already or purely, that is solely,) based on apart of the object that is adjacent to the border. The part may coverless than 80%, in particular less than 50% and preferably less than 20%of the object. This gives the advantages that the network notnecessarily has to be trained to detect the complete object and/or toclassify the object based on the complete object, for instance thecomplete outline, but that a part of the object may be sufficient forthe object detection and/or classification. This makes training of theneural network easier and saves resources and can lead to fasterresults.

In another embodiment, it is provided that the capturing is effected bya capturing device with at least one camera with each camera having atleast 0.1 mega pixel, in particular at least 1 mega pixel, preferably atleast 4 mega pixel. This gives the advantage that the above-mentioneddense processing, the dense depth map and dense feature representationis enabled.

Another aspect of the invention relates to a driver assistance system ora motor vehicle, with a capturing device for capturing an environment ofthe motor vehicle and a computing device for detecting an accessiblefreespace in the captured environment. Herein, the driver assistancesystem comprises a neural network that is formed to detect and/orclassify at least one object in the captured environment that is locatedat the border of the freespace, in particular the detected freespace.

Furthermore, the computing device is formed to assign a part of theborder to the detected and/or classified object as well as to categorizea part of the freespace adjacent to the part of the border that isassigned to detected and/or classified object in dependence upon theclassified object.

Advantages and advantageous embodiments of the driver assistance systemcorrespond to advantages and advantageous embodiments of the describedmethod. The invention furthermore relates to a motor vehicle with such adriver assistance system.

BRIEF DESCRIPTION OF THE DRAWINGS

The single FIGURE shows a schematic representation of a motor vehiclewith a driver assistance system in accordance with one or moreembodiments herein.

DETAILED DESCRIPTION

The features and feature combinations mentioned above in the descriptionas well as the features and feature combinations mentioned below in thedescription of FIGURES and/or shown in the FIGURES alone are usable notonly in the respectively specified combination, but also in othercombinations without departing from the scope of the invention. Thus,implementations are also to be considered as encompassed and disclosedby the invention, which are not explicitly shown in the figures andexplained, but arise from and can be generated by separated featurecombinations from the explained implementations. Implementations andfeature combinations are also to be considered as disclosed, which thusdo not have all of the features of an originally formulated independentclaim. Moreover, implementations and feature combinations are to beconsidered as disclosed, in particular by the implementations set outabove, which extend beyond or deviate from the feature combinations setout in the relations of the claims.

Below embodiments of the invention are explained in more detail based ona schematic drawing. Therein the only FIGURE shows a schematicrepresentation of a motor vehicle with an exemplary embodiment of adriver assistance system in an exemplary scenario.

Therein, the motor vehicle 1 is equipped with a driver assistance system2 that comprises a capturing device 3 for capturing an environment 4 ofthe motor vehicle 1 and with a computing device 5 for detecting anaccessible freespace 6. Here, the freespace 6 is adjacent to the motorvehicle 1 in the present example.

The computing device 5 comprises a neural network 9 in the currentexample. The neural network 9 is formed to detect and classify at leastone object, presently several objects 7 a to 7 b, in the capturedenvironment 4, that is, objects 7 a to 7 b presently located at a border8 of the freespace 6. The computing device 5 is formed to assign arespective part 10 a to 10 e of the border 8 to the detected andclassified objects 7 a to 7 e as well as to categorize respective parts11 a to 11 e of the freespace 6 adjacent to the respective parts 10 a to10 e of the border 8 (that are assigned to the detected and classifiedobjects 7 a to 7 e) in dependence upon the class of the classifiedobjects 7 a to 7 e.

So, in the present example, the regions or parts 11 a, 11 b, 11 c arecategorized in dependence upon the class of the objects 7 a, 7 b, and 7c. Presently, as the objects 7 a, 7 b, 7 c are classified as vehicles,the parts 11 a, 11 b, 11 c of the freespace 6 are categorized aspotentially dangerous freespace here. So, for instance a speed limit maybe set for the motor vehicle 1 when heading towards or into therespective regions or parts 11 a, 11 b, 11 c. The part 11 d of freespace6 is categorized in dependence upon the class of the object 7 d, whichis presently classified as pedestrian. Hence, in the present example,the region or part 11 d of the freespace 6 is categorized as dangerousfreespace. So, for instance, it may be prohibited for the motor vehicle1 to head into the part 11 d of the freespace 6. The region or part 11 eof freespace 6 is, in the present example categorized in dependence uponthe class of object 7 e, which is classified as a curb here. Hence, thepart 11 e is categorized in a conditionally accessible freespace here.For example, a second speed limit that may be higher than theabove-mentioned first speed limit is set when the motor vehicle 1 headsinto that part 11 e of freespace 6.

In the present example the part 10 of the border 8 is not assigned toany object. The part 11 of the freespace 6 adjacent to that part 10 ofthe border is, in the present example, consequently categorized assafely accessible freespace. Hence, for example no speed limit is set ina semi-automated or fully automated driving here when the vehicle 1heads into the part 11 of the freespace 6.

In the present example, the neural network 9, that preferably comprisesor is a convolutional neural network, is trained to classify the objects7 a to 7 e based on a part of the respective object 7 a to 7 e that isadjacent to the border 8. So, the neural network 9 is able to classifythe objects 7 a to 7 e with analysing only the part of the respectiveobjects 7 a-7 e adjacent to the border 8 of the freespace 6. Herein, anobject 7 a to 7 e may be classified not only in one class but in morethan one class, where a respective confidence value is assigned to eachof the classes the classified objects 7 a to 7 e are assigned to. Here,the confidence value indicates the probability for the respective classassigned to the classified object 7 a-7 e to be true. So, the neuralnetwork 9 may be trained using a generic function learning approach withabstract feature maps. Therein, the abstract features may not necessaryneed to be understood intuitively by humans. The abstract features maybe evaluated for classifying or for detecting the respective object in arespective area of an image, for instance, a camera image of thecapturing device 3.

The invention claimed is:
 1. A method for operating a driver assistancesystem of a motor vehicle, comprising: a) capturing an environment ofthe motor vehicle by a capturing device of the driver assistance system;b) detecting an accessible freespace in the captured environment by acomputing device of the driver assistance system; c) detecting andclassifying a plurality of objects in the captured environment that arelocated at a border of the freespace by a neural network of the driverassistance system to produce a plurality of classified objects; d)assigning a part of the border of the freespace to a classified objectamong the plurality of classified objects; and e) categorizing a part ofthe freespace adjacent to the part of the border that is assigned to theclassified object in dependence upon the class of the classified object,wherein a respective risk is assigned to the categories for thefreespace that the part of freespace is categorized into a risk for acollision with the classified object adjacent to the respective part offreespace, wherein in the classifying of the at least one objectaccording to method step c), more than one class is assigned to theclassified object and a confidence value that indicates the probabilityfor the respective class assigned to the classified object to be true isassigned to each class assigned to the classified object, wherein in thecategorizing of the part of the freespace according to method step e),the confidence values for the classes of the classified object arecompared, wherein the freespace is categorized into the category for thefreespace that corresponds to the class with the highest confidencevalue, when this confidence value differs from the second highestconfidence value of the classes assigned to the classified object morethan a preset threshold, and wherein the freespace is categorized intothe category that the highest respective risk is assigned to, when thehighest confidence value differs from the second highest confidencevalue less than the preset threshold.
 2. The method according to claim1, wherein a part of the freespace adjacent to another part of theborder that is not assigned to the classified object and is adjacent tothe rest of the border, is categorized as safely accessible freespace.3. The method according to claim 1, wherein the categories for thefreespace comprise a category for safely accessible freespace and/or acategory for potentially dangerous freespace and/or a category fordangerous freespace.
 4. The method according to claim 1, wherein theclasses for the classified object comprise a class for static obstaclesthat are driven over, and/or a class for obstacles that are not drivenover, and/or a class for dynamic obstacles comprising pedestrians and/ora class for cyclists and/or a class for motor vehicles.
 5. The methodaccording to claim 1, wherein: the categorizing of the part of thefreespace according to method step e) is effected in dependence upon theclasses of that classified object and the respective confidence values.6. The method according to claim 1, wherein the capturing devicecomprises several cameras and the detecting according to method step b)and detecting and classifying according to method step c) and assigningaccording to method step d) and categorizing according to method step e)is effected for pictures of the captured environment that are taken bythe different cameras, and results of the categorization that are basedon different pictures of the same and/or of different cameras are fusedinto an overall-result of categorization with the assumption that theresults are dependent on each other as described by a preset rule, wherethe fusing is effected using a conditional random field.
 7. The methodaccording to claim 1, wherein at the neural network comprises aconvolutional neural network which is a deep neural network withmultiple hidden layers.
 8. The method according to claim 1, wherein theneural network is trained to classify the object based on a part of theobject that is adjacent to the border, where the part may cover lessthan 20% of the object.
 9. The method according to claim 1, wherein thecapturing according to method step a) is effected by a capturing devicewith a camera with 4 mega pixel.
 10. A driver assistance system for amotor vehicle, comprising: a capturing device for capturing anenvironment of the motor vehicle; a computing device for detecting anaccessible freespace in the captured environment; and a neural networkthat is formed to detect and classify a plurality of objects in thecaptured environment that are located at a border of the detectedfreespace to produce a plurality of classified objects, wherein thecomputing device is formed to assign a part of the border to aclassified object among the plurality of classified objects as well asto categorize a part of the freespace adjacent to the part of the borderthat is assigned to the classified object in dependence upon the classof the classified object, wherein a respective risk is assigned to thecategories for the freespace that the part of freespace is categorizedinto a risk for a collision with the classified object adjacent to therespective part of freespace, wherein in the classifying of the at leastone object according to method step c), more than one class is assignedto the classified object and a confidence value that indicates theprobability for the respective class assigned to the classified objectto be true is assigned to each class assigned to the classified object,wherein in the categorizing of the part of the freespace according tomethod step e), the confidence values for the classes of the classifiedobject are compared, and wherein the freespace is categorized into thecategory for the freespace that corresponds to the class with thehighest confidence value, if this confidence value differs from thesecond highest confidence value of the classes assigned to theclassified object more than a preset threshold, and wherein thefreespace is categorized into the category that the highest respectiverisk is assigned to, when the highest confidence value differs from thesecond highest confidence value less than the preset threshold.
 11. Amotor vehicle with a driver assistance system according to claim 10.